Overview

Brought to you by YData

Dataset statistics

Number of variables43
Number of observations198910
Missing cells2246072
Missing cells (%)26.3%
Duplicate rows100
Duplicate rows (%)0.1%
Total size in memory352.2 MiB
Average record size in memory1.8 KiB

Variable types

Text10
Numeric13
Categorical9
DateTime7
Boolean4

Alerts

Structural Notification has constant value "True"Constant
Voluntary Soft-Story Retrofit has constant value "True"Constant
Fire Only Permit has constant value "True"Constant
Site Permit has constant value "True"Constant
Dataset has 100 (0.1%) duplicate rowsDuplicates
Estimated Cost is highly overall correlated with Revised CostHigh correlation
Existing Construction Type is highly overall correlated with Number of Existing Stories and 2 other fieldsHigh correlation
Existing Construction Type Description is highly overall correlated with Proposed Construction Type and 1 other fieldsHigh correlation
Existing Units is highly overall correlated with Proposed UnitsHigh correlation
Neighborhoods - Analysis Boundaries is highly overall correlated with Supervisor District and 1 other fieldsHigh correlation
Number of Existing Stories is highly overall correlated with Existing Construction Type and 1 other fieldsHigh correlation
Number of Proposed Stories is highly overall correlated with Existing Construction Type and 1 other fieldsHigh correlation
Permit Type is highly overall correlated with Permit Type DefinitionHigh correlation
Permit Type Definition is highly overall correlated with Permit TypeHigh correlation
Plansets is highly overall correlated with Street Number SuffixHigh correlation
Proposed Construction Type is highly overall correlated with Existing Construction Type Description and 1 other fieldsHigh correlation
Proposed Construction Type Description is highly overall correlated with Existing Construction Type Description and 1 other fieldsHigh correlation
Proposed Units is highly overall correlated with Existing UnitsHigh correlation
Revised Cost is highly overall correlated with Estimated CostHigh correlation
Street Number Suffix is highly overall correlated with Existing Construction Type and 1 other fieldsHigh correlation
Supervisor District is highly overall correlated with Neighborhoods - Analysis Boundaries and 1 other fieldsHigh correlation
Zipcode is highly overall correlated with Neighborhoods - Analysis Boundaries and 1 other fieldsHigh correlation
Permit Type Definition is highly imbalanced (83.5%)Imbalance
Street Number Suffix is highly imbalanced (59.2%)Imbalance
Street Suffix is highly imbalanced (69.9%)Imbalance
Current Status is highly imbalanced (60.4%)Imbalance
Existing Construction Type Description is highly imbalanced (60.4%)Imbalance
Street Number Suffix has 196694 (98.9%) missing valuesMissing
Street Suffix has 2768 (1.4%) missing valuesMissing
Unit has 169430 (85.2%) missing valuesMissing
Unit Suffix has 196949 (99.0%) missing valuesMissing
Issued Date has 14942 (7.5%) missing valuesMissing
Completed Date has 101715 (51.1%) missing valuesMissing
First Construction Document Date has 14948 (7.5%) missing valuesMissing
Structural Notification has 191988 (96.5%) missing valuesMissing
Number of Existing Stories has 42788 (21.5%) missing valuesMissing
Number of Proposed Stories has 42871 (21.6%) missing valuesMissing
Voluntary Soft-Story Retrofit has 198875 (> 99.9%) missing valuesMissing
Fire Only Permit has 180082 (90.5%) missing valuesMissing
Permit Expiration Date has 51884 (26.1%) missing valuesMissing
Estimated Cost has 38068 (19.1%) missing valuesMissing
Revised Cost has 6066 (3.0%) missing valuesMissing
Existing Use has 41117 (20.7%) missing valuesMissing
Existing Units has 51543 (25.9%) missing valuesMissing
Proposed Use has 42441 (21.3%) missing valuesMissing
Proposed Units has 50915 (25.6%) missing valuesMissing
Plansets has 37311 (18.8%) missing valuesMissing
TIDF Compliance has 198908 (> 99.9%) missing valuesMissing
Existing Construction Type has 43369 (21.8%) missing valuesMissing
Existing Construction Type Description has 43370 (21.8%) missing valuesMissing
Proposed Construction Type has 43165 (21.7%) missing valuesMissing
Proposed Construction Type Description has 43165 (21.7%) missing valuesMissing
Site Permit has 193550 (97.3%) missing valuesMissing
Estimated Cost is highly skewed (γ1 = 80.2643549)Skewed
Revised Cost is highly skewed (γ1 = 108.7016029)Skewed
Plansets is highly skewed (γ1 = 400.8420748)Skewed
Existing Construction Type is highly skewed (γ1 = 35.25093762)Skewed
Unit has 21411 (10.8%) zerosZeros
Revised Cost has 8514 (4.3%) zerosZeros
Existing Units has 29135 (14.6%) zerosZeros
Proposed Units has 26885 (13.5%) zerosZeros
Plansets has 63247 (31.8%) zerosZeros

Reproduction

Analysis started2024-08-29 16:15:05.020351
Analysis finished2024-08-29 16:16:09.687095
Duration1 minute and 4.67 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Distinct34484
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
2024-08-29T13:16:10.305136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.181952
Min length7

Characters and Unicode

Total characters2025292
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31975 ?
Unique (%)16.1%

Sample

1st rowM394467
2nd row2.01505E+11
3rd rowM798247
4th row2.01308E+11
5th row2.01307E+11
ValueCountFrequency (%)
2.01709e+11 3549
 
1.8%
2.01708e+11 3424
 
1.7%
2.01406e+11 3092
 
1.6%
2.01603e+11 2984
 
1.5%
2.01503e+11 2982
 
1.5%
2.01706e+11 2969
 
1.5%
2.01506e+11 2944
 
1.5%
2.01705e+11 2935
 
1.5%
2.01609e+11 2927
 
1.5%
2.01504e+11 2914
 
1.5%
Other values (34474) 168190
84.6%
2024-08-29T13:16:10.898570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 561084
27.7%
0 304396
15.0%
2 206496
 
10.2%
. 161645
 
8.0%
E 161645
 
8.0%
+ 161645
 
8.0%
7 92993
 
4.6%
6 71859
 
3.5%
4 67853
 
3.4%
5 64530
 
3.2%
Other values (4) 171146
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2025292
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 561084
27.7%
0 304396
15.0%
2 206496
 
10.2%
. 161645
 
8.0%
E 161645
 
8.0%
+ 161645
 
8.0%
7 92993
 
4.6%
6 71859
 
3.5%
4 67853
 
3.4%
5 64530
 
3.2%
Other values (4) 171146
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2025292
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 561084
27.7%
0 304396
15.0%
2 206496
 
10.2%
. 161645
 
8.0%
E 161645
 
8.0%
+ 161645
 
8.0%
7 92993
 
4.6%
6 71859
 
3.5%
4 67853
 
3.4%
5 64530
 
3.2%
Other values (4) 171146
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2025292
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 561084
27.7%
0 304396
15.0%
2 206496
 
10.2%
. 161645
 
8.0%
E 161645
 
8.0%
+ 161645
 
8.0%
7 92993
 
4.6%
6 71859
 
3.5%
4 67853
 
3.4%
5 64530
 
3.2%
Other values (4) 171146
 
8.5%

Permit Type
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5222865
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-29T13:16:11.078593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median8
Q38
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.457526
Coefficient of variation (CV)0.19376103
Kurtosis6.036264
Mean7.5222865
Median Absolute Deviation (MAD)0
Skewness-2.79896
Sum1496258
Variance2.124382
MonotonicityNot monotonic
2024-08-29T13:16:11.208499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
8 178852
89.9%
3 14664
 
7.4%
4 2892
 
1.5%
2 950
 
0.5%
6 600
 
0.3%
7 511
 
0.3%
1 350
 
0.2%
5 91
 
< 0.1%
ValueCountFrequency (%)
1 350
 
0.2%
2 950
 
0.5%
3 14664
 
7.4%
4 2892
 
1.5%
5 91
 
< 0.1%
6 600
 
0.3%
7 511
 
0.3%
8 178852
89.9%
ValueCountFrequency (%)
8 178852
89.9%
7 511
 
0.3%
6 600
 
0.3%
5 91
 
< 0.1%
4 2892
 
1.5%
3 14664
 
7.4%
2 950
 
0.5%
1 350
 
0.2%

Permit Type Definition
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.1 MiB
otc alterations permit
178836 
additions alterations or repairs
 
14663
sign - erect
 
2892
new construction wood frame
 
950
demolitions
 
600
Other values (8)
 
969

Length

Max length35
Median length22
Mean length22.572978
Min length11

Characters and Unicode

Total characters4489991
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowotc alterations permit
2nd rowotc alterations permit
3rd rowotc alterations permit
4th rowotc alterations permit
5th rowotc alterations permit

Common Values

ValueCountFrequency (%)
otc alterations permit 178836
89.9%
additions alterations or repairs 14663
 
7.4%
sign - erect 2892
 
1.5%
new construction wood frame 950
 
0.5%
demolitions 600
 
0.3%
wall or painted sign 511
 
0.3%
new construction 347
 
0.2%
grade or quarry or fill or excavate 91
 
< 0.1%
otc alterations permit 8
 
< 0.1%
otc alterations permit # 8
 
< 0.1%
Other values (3) 4
 
< 0.1%

Length

2024-08-29T13:16:11.353400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
alterations 193516
31.6%
otc 178852
29.2%
permit 178852
29.2%
or 15448
 
2.5%
additions 14664
 
2.4%
repairs 14664
 
2.4%
sign 3403
 
0.6%
2902
 
0.5%
erect 2892
 
0.5%
construction 1300
 
0.2%
Other values (10) 5186
 
0.8%

Most occurring characters

ValueCountFrequency (%)
t 766094
17.1%
i 422865
9.4%
r 422559
9.4%
a 418696
9.3%
412789
9.2%
o 408180
9.1%
e 396450
8.8%
s 228147
 
5.1%
n 216594
 
4.8%
l 195320
 
4.4%
Other values (14) 602297
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4489991
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 766094
17.1%
i 422865
9.4%
r 422559
9.4%
a 418696
9.3%
412789
9.2%
o 408180
9.1%
e 396450
8.8%
s 228147
 
5.1%
n 216594
 
4.8%
l 195320
 
4.4%
Other values (14) 602297
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4489991
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 766094
17.1%
i 422865
9.4%
r 422559
9.4%
a 418696
9.3%
412789
9.2%
o 408180
9.1%
e 396450
8.8%
s 228147
 
5.1%
n 216594
 
4.8%
l 195320
 
4.4%
Other values (14) 602297
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4489991
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 766094
17.1%
i 422865
9.4%
r 422559
9.4%
a 418696
9.3%
412789
9.2%
o 408180
9.1%
e 396450
8.8%
s 228147
 
5.1%
n 216594
 
4.8%
l 195320
 
4.4%
Other values (14) 602297
13.4%
Distinct1291
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2012-03-28 00:00:00
Maximum2018-02-23 00:00:00
2024-08-29T13:16:11.512117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:11.658304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Block
Text

Distinct4893
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size11.5 MiB
2024-08-29T13:16:12.642037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.7171082
Min length1

Characters and Unicode

Total characters739370
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)0.1%

Sample

1st row3751
2nd row3556
3rd row655
4th row4267
5th row1518
ValueCountFrequency (%)
3708 1195
 
0.6%
3735 750
 
0.4%
7331 680
 
0.3%
289 640
 
0.3%
3709 584
 
0.3%
3717 578
 
0.3%
3707 576
 
0.3%
3721 567
 
0.3%
3706 561
 
0.3%
259 554
 
0.3%
Other values (4883) 192225
96.6%
2024-08-29T13:16:13.689728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 97524
13.2%
1 96595
13.1%
2 85374
11.5%
5 80950
10.9%
6 77737
10.5%
7 73548
9.9%
0 60818
8.2%
4 60017
8.1%
8 50674
6.9%
9 48902
6.6%
Other values (9) 7231
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 739370
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 97524
13.2%
1 96595
13.1%
2 85374
11.5%
5 80950
10.9%
6 77737
10.5%
7 73548
9.9%
0 60818
8.2%
4 60017
8.1%
8 50674
6.9%
9 48902
6.6%
Other values (9) 7231
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 739370
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 97524
13.2%
1 96595
13.1%
2 85374
11.5%
5 80950
10.9%
6 77737
10.5%
7 73548
9.9%
0 60818
8.2%
4 60017
8.1%
8 50674
6.9%
9 48902
6.6%
Other values (9) 7231
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 739370
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 97524
13.2%
1 96595
13.1%
2 85374
11.5%
5 80950
10.9%
6 77737
10.5%
7 73548
9.9%
0 60818
8.2%
4 60017
8.1%
8 50674
6.9%
9 48902
6.6%
Other values (9) 7231
 
1.0%

Lot
Text

Distinct1050
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
2024-08-29T13:16:14.667592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length2
Mean length1.9519883
Min length1

Characters and Unicode

Total characters388270
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)0.1%

Sample

1st row172
2nd row6
3rd row61
4th row29
5th row37
ValueCountFrequency (%)
1 10116
 
5.1%
7 5317
 
2.7%
2 5184
 
2.6%
3 5042
 
2.5%
6 4835
 
2.4%
8 4773
 
2.4%
9 4590
 
2.3%
5 4549
 
2.3%
4 4384
 
2.2%
11 4238
 
2.1%
Other values (1040) 145882
73.3%
2024-08-29T13:16:15.804908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 80415
20.7%
2 59437
15.3%
3 43028
11.1%
0 42471
10.9%
4 33218
8.6%
5 26863
 
6.9%
6 25008
 
6.4%
7 22210
 
5.7%
8 20304
 
5.2%
9 19441
 
5.0%
Other values (26) 15875
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 388270
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 80415
20.7%
2 59437
15.3%
3 43028
11.1%
0 42471
10.9%
4 33218
8.6%
5 26863
 
6.9%
6 25008
 
6.4%
7 22210
 
5.7%
8 20304
 
5.2%
9 19441
 
5.0%
Other values (26) 15875
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 388270
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 80415
20.7%
2 59437
15.3%
3 43028
11.1%
0 42471
10.9%
4 33218
8.6%
5 26863
 
6.9%
6 25008
 
6.4%
7 22210
 
5.7%
8 20304
 
5.2%
9 19441
 
5.0%
Other values (26) 15875
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 388270
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 80415
20.7%
2 59437
15.3%
3 43028
11.1%
0 42471
10.9%
4 33218
8.6%
5 26863
 
6.9%
6 25008
 
6.4%
7 22210
 
5.7%
8 20304
 
5.2%
9 19441
 
5.0%
Other values (26) 15875
 
4.1%

Street Number
Real number (ℝ)

Distinct5099
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1121.7155
Minimum0
Maximum8400
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-29T13:16:15.981216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32
Q1235
median710
Q31700
95-th percentile3481
Maximum8400
Range8400
Interquartile range (IQR)1465

Descriptive statistics

Standard deviation1135.7985
Coefficient of variation (CV)1.0125548
Kurtosis2.1350541
Mean1121.7155
Median Absolute Deviation (MAD)580
Skewness1.4269693
Sum2.2312044 × 108
Variance1290038.2
MonotonicityNot monotonic
2024-08-29T13:16:16.134269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2398
 
1.2%
101 1153
 
0.6%
100 1143
 
0.6%
50 1103
 
0.6%
201 1026
 
0.5%
555 994
 
0.5%
2 814
 
0.4%
55 734
 
0.4%
350 705
 
0.4%
150 672
 
0.3%
Other values (5089) 188168
94.6%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 2398
1.2%
2 814
 
0.4%
3 309
 
0.2%
4 255
 
0.1%
5 194
 
0.1%
6 113
 
0.1%
7 132
 
0.1%
8 281
 
0.1%
9 128
 
0.1%
ValueCountFrequency (%)
8400 3
< 0.1%
8331 1
 
< 0.1%
8325 5
< 0.1%
8320 1
 
< 0.1%
8300 1
 
< 0.1%
8245 1
 
< 0.1%
8231 1
 
< 0.1%
8228 1
 
< 0.1%
8222 7
< 0.1%
8219 1
 
< 0.1%

Street Number Suffix
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct18
Distinct (%)0.8%
Missing196694
Missing (%)98.9%
Memory size12.1 MiB
A
1501 
B
291 
V
228 
C
 
56
E
 
28
Other values (13)
 
112

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row0
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 1501
 
0.8%
B 291
 
0.1%
V 228
 
0.1%
C 56
 
< 0.1%
E 28
 
< 0.1%
F 24
 
< 0.1%
G 12
 
< 0.1%
D 11
 
< 0.1%
H 11
 
< 0.1%
K 11
 
< 0.1%
Other values (8) 43
 
< 0.1%
(Missing) 196694
98.9%

Length

2024-08-29T13:16:16.288186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 1501
67.7%
b 291
 
13.1%
v 228
 
10.3%
c 56
 
2.5%
e 28
 
1.3%
f 24
 
1.1%
g 12
 
0.5%
h 11
 
0.5%
k 11
 
0.5%
d 11
 
0.5%
Other values (8) 43
 
1.9%

Most occurring characters

ValueCountFrequency (%)
A 1501
67.7%
B 291
 
13.1%
V 228
 
10.3%
C 56
 
2.5%
E 28
 
1.3%
F 24
 
1.1%
G 12
 
0.5%
H 11
 
0.5%
K 11
 
0.5%
D 11
 
0.5%
Other values (8) 43
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1501
67.7%
B 291
 
13.1%
V 228
 
10.3%
C 56
 
2.5%
E 28
 
1.3%
F 24
 
1.1%
G 12
 
0.5%
H 11
 
0.5%
K 11
 
0.5%
D 11
 
0.5%
Other values (8) 43
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1501
67.7%
B 291
 
13.1%
V 228
 
10.3%
C 56
 
2.5%
E 28
 
1.3%
F 24
 
1.1%
G 12
 
0.5%
H 11
 
0.5%
K 11
 
0.5%
D 11
 
0.5%
Other values (8) 43
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1501
67.7%
B 291
 
13.1%
V 228
 
10.3%
C 56
 
2.5%
E 28
 
1.3%
F 24
 
1.1%
G 12
 
0.5%
H 11
 
0.5%
K 11
 
0.5%
D 11
 
0.5%
Other values (8) 43
 
1.9%
Distinct1714
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
2024-08-29T13:16:17.026635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length22
Mean length6.5098185
Min length3

Characters and Unicode

Total characters1294868
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)0.1%

Sample

1st row03rD
2nd rowgUeRrErO
3rd rowpInE
4th rowbRyAnT
5th row26tH
ValueCountFrequency (%)
market 5443
 
2.6%
california 4587
 
2.2%
mission 4324
 
2.0%
montgomery 2933
 
1.4%
geary 1966
 
0.9%
20th 1859
 
0.9%
03rd 1819
 
0.9%
folsom 1776
 
0.8%
van 1698
 
0.8%
pine 1678
 
0.8%
Other values (1705) 184679
86.8%
2024-08-29T13:16:17.892801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 83888
 
6.5%
O 63275
 
4.9%
e 55861
 
4.3%
n 55448
 
4.3%
r 54146
 
4.2%
E 54117
 
4.2%
t 51943
 
4.0%
s 49681
 
3.8%
R 45151
 
3.5%
T 42390
 
3.3%
Other values (55) 738968
57.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1294868
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 83888
 
6.5%
O 63275
 
4.9%
e 55861
 
4.3%
n 55448
 
4.3%
r 54146
 
4.2%
E 54117
 
4.2%
t 51943
 
4.0%
s 49681
 
3.8%
R 45151
 
3.5%
T 42390
 
3.3%
Other values (55) 738968
57.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1294868
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 83888
 
6.5%
O 63275
 
4.9%
e 55861
 
4.3%
n 55448
 
4.3%
r 54146
 
4.2%
E 54117
 
4.2%
t 51943
 
4.0%
s 49681
 
3.8%
R 45151
 
3.5%
T 42390
 
3.3%
Other values (55) 738968
57.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1294868
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 83888
 
6.5%
O 63275
 
4.9%
e 55861
 
4.3%
n 55448
 
4.3%
r 54146
 
4.2%
E 54117
 
4.2%
t 51943
 
4.0%
s 49681
 
3.8%
R 45151
 
3.5%
T 42390
 
3.3%
Other values (55) 738968
57.1%

Street Suffix
Categorical

IMBALANCE  MISSING 

Distinct21
Distinct (%)< 0.1%
Missing2768
Missing (%)1.4%
Memory size11.2 MiB
St
138365 
Av
43222 
Bl
 
3555
Wy
 
3540
Dr
 
3267
Other values (16)
 
4193

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters392284
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSt
2nd rowSt
3rd rowSt
4th rowSt
5th rowAv

Common Values

ValueCountFrequency (%)
St 138365
69.6%
Av 43222
 
21.7%
Bl 3555
 
1.8%
Wy 3540
 
1.8%
Dr 3267
 
1.6%
Tr 1466
 
0.7%
Ct 667
 
0.3%
Pl 538
 
0.3%
Rd 389
 
0.2%
Ln 354
 
0.2%
Other values (11) 779
 
0.4%
(Missing) 2768
 
1.4%

Length

2024-08-29T13:16:18.036186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st 138365
70.5%
av 43222
 
22.0%
bl 3555
 
1.8%
wy 3540
 
1.8%
dr 3267
 
1.7%
tr 1466
 
0.7%
ct 667
 
0.3%
pl 538
 
0.3%
rd 389
 
0.2%
ln 354
 
0.2%
Other values (11) 779
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 139032
35.4%
S 138369
35.3%
A 43305
 
11.0%
v 43222
 
11.0%
r 4830
 
1.2%
l 4177
 
1.1%
y 3780
 
1.0%
B 3555
 
0.9%
W 3549
 
0.9%
D 3267
 
0.8%
Other values (13) 5198
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 392284
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 139032
35.4%
S 138369
35.3%
A 43305
 
11.0%
v 43222
 
11.0%
r 4830
 
1.2%
l 4177
 
1.1%
y 3780
 
1.0%
B 3555
 
0.9%
W 3549
 
0.9%
D 3267
 
0.8%
Other values (13) 5198
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 392284
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 139032
35.4%
S 138369
35.3%
A 43305
 
11.0%
v 43222
 
11.0%
r 4830
 
1.2%
l 4177
 
1.1%
y 3780
 
1.0%
B 3555
 
0.9%
W 3549
 
0.9%
D 3267
 
0.8%
Other values (13) 5198
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 392284
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 139032
35.4%
S 138369
35.3%
A 43305
 
11.0%
v 43222
 
11.0%
r 4830
 
1.2%
l 4177
 
1.1%
y 3780
 
1.0%
B 3555
 
0.9%
W 3549
 
0.9%
D 3267
 
0.8%
Other values (13) 5198
 
1.3%

Unit
Real number (ℝ)

MISSING  ZEROS 

Distinct660
Distinct (%)2.2%
Missing169430
Missing (%)85.2%
Infinite0
Infinite (%)0.0%
Mean78.514518
Minimum0
Maximum6004
Zeros21411
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-29T13:16:18.178186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile446.1
Maximum6004
Range6004
Interquartile range (IQR)1

Descriptive statistics

Standard deviation326.9761
Coefficient of variation (CV)4.1645304
Kurtosis92.15637
Mean78.514518
Median Absolute Deviation (MAD)0
Skewness8.1334162
Sum2314608
Variance106913.37
MonotonicityNot monotonic
2024-08-29T13:16:18.330677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21411
 
10.8%
1 1312
 
0.7%
2 549
 
0.3%
3 448
 
0.2%
101 365
 
0.2%
4 288
 
0.1%
6 214
 
0.1%
5 211
 
0.1%
201 210
 
0.1%
8 104
 
0.1%
Other values (650) 4368
 
2.2%
(Missing) 169430
85.2%
ValueCountFrequency (%)
0 21411
10.8%
1 1312
 
0.7%
2 549
 
0.3%
3 448
 
0.2%
4 288
 
0.1%
5 211
 
0.1%
6 214
 
0.1%
7 103
 
0.1%
8 104
 
0.1%
9 69
 
< 0.1%
ValueCountFrequency (%)
6004 1
 
< 0.1%
6003 3
< 0.1%
5903 1
 
< 0.1%
5604 1
 
< 0.1%
5602 1
 
< 0.1%
5413 1
 
< 0.1%
5412 1
 
< 0.1%
5313 1
 
< 0.1%
5312 1
 
< 0.1%
5306 1
 
< 0.1%

Unit Suffix
Text

MISSING 

Distinct163
Distinct (%)8.3%
Missing196949
Missing (%)99.0%
Memory size6.1 MiB
2024-08-29T13:16:18.775248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length1
Mean length2.6241713
Min length1

Characters and Unicode

Total characters5146
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)3.1%

Sample

1st rowFRONT UNIT
2nd rowA
3rd rowFRONT UNIT
4th rowTENTATIVE
5th rowREAR BLDG
ValueCountFrequency (%)
a 350
16.3%
c 229
 
10.7%
b 193
 
9.0%
d 137
 
6.4%
e 93
 
4.3%
bldg 93
 
4.3%
hoa 85
 
4.0%
f 78
 
3.6%
comml 51
 
2.4%
front 48
 
2.2%
Other values (136) 792
36.9%
2024-08-29T13:16:19.345053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 551
 
10.7%
C 409
 
7.9%
E 331
 
6.4%
R 311
 
6.0%
B 309
 
6.0%
D 295
 
5.7%
O 264
 
5.1%
L 261
 
5.1%
F 208
 
4.0%
M 192
 
3.7%
Other values (38) 2015
39.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 551
 
10.7%
C 409
 
7.9%
E 331
 
6.4%
R 311
 
6.0%
B 309
 
6.0%
D 295
 
5.7%
O 264
 
5.1%
L 261
 
5.1%
F 208
 
4.0%
M 192
 
3.7%
Other values (38) 2015
39.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 551
 
10.7%
C 409
 
7.9%
E 331
 
6.4%
R 311
 
6.0%
B 309
 
6.0%
D 295
 
5.7%
O 264
 
5.1%
L 261
 
5.1%
F 208
 
4.0%
M 192
 
3.7%
Other values (38) 2015
39.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 551
 
10.7%
C 409
 
7.9%
E 331
 
6.4%
R 311
 
6.0%
B 309
 
6.0%
D 295
 
5.7%
O 264
 
5.1%
L 261
 
5.1%
F 208
 
4.0%
M 192
 
3.7%
Other values (38) 2015
39.2%
Distinct134272
Distinct (%)67.6%
Missing290
Missing (%)0.1%
Memory size30.0 MiB
2024-08-29T13:16:20.097839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length300
Median length243
Mean length101.45318
Min length1

Characters and Unicode

Total characters20150631
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122032 ?
Unique (%)61.4%

Sample

1st rowstreet space
2nd rowremodel (e) bathroom, convert (e) bedrm into bed room and office, all work at ground floor, comply with complaint 200452633. items 3 & 4: existing furnaces to be used.
3rd rowstreet space permit
4th rowcomply with nov 200009226 and 200346303 remoe e non permitted shed at the rear for unit 2607a. remodel bath, kitchen, new bedroom . unit 2607: remodel bathroom, kitchen, new bedroom, and bath. new lighting and fixtures for both unit
5th rowreplace 2 windows in bathroom not visible; no structural changes max u factor .40
ValueCountFrequency (%)
94921
 
3.0%
to 93338
 
2.9%
and 68894
 
2.2%
new 61786
 
1.9%
of 54700
 
1.7%
street 44273
 
1.4%
space 41100
 
1.3%
replace 36545
 
1.1%
in 36109
 
1.1%
for 35061
 
1.1%
Other values (109535) 2636742
82.3%
2024-08-29T13:16:21.178823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3042851
15.1%
e 1735300
 
8.6%
o 1280056
 
6.4%
r 1277846
 
6.3%
t 1208273
 
6.0%
n 1153984
 
5.7%
a 1149599
 
5.7%
i 1122199
 
5.6%
s 828425
 
4.1%
l 785552
 
3.9%
Other values (66) 6566546
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20150631
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3042851
15.1%
e 1735300
 
8.6%
o 1280056
 
6.4%
r 1277846
 
6.3%
t 1208273
 
6.0%
n 1153984
 
5.7%
a 1149599
 
5.7%
i 1122199
 
5.6%
s 828425
 
4.1%
l 785552
 
3.9%
Other values (66) 6566546
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20150631
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3042851
15.1%
e 1735300
 
8.6%
o 1280056
 
6.4%
r 1277846
 
6.3%
t 1208273
 
6.0%
n 1153984
 
5.7%
a 1149599
 
5.7%
i 1122199
 
5.6%
s 828425
 
4.1%
l 785552
 
3.9%
Other values (66) 6566546
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20150631
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3042851
15.1%
e 1735300
 
8.6%
o 1280056
 
6.4%
r 1277846
 
6.3%
t 1208273
 
6.0%
n 1153984
 
5.7%
a 1149599
 
5.7%
i 1122199
 
5.6%
s 828425
 
4.1%
l 785552
 
3.9%
Other values (66) 6566546
32.6%

Current Status
Categorical

IMBALANCE 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.1 MiB
complete
97081 
issued
83563 
filed
12045 
withdrawn
 
1754
cancelled
 
1536
Other values (9)
 
2931

Length

Max length11
Median length10
Mean length6.9923282
Min length5

Characters and Unicode

Total characters1390844
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowissued
2nd rowcomplete
3rd rowissued
4th rowcomplete
5th rowcomplete

Common Values

ValueCountFrequency (%)
complete 97081
48.8%
issued 83563
42.0%
filed 12045
 
6.1%
withdrawn 1754
 
0.9%
cancelled 1536
 
0.8%
expired 1370
 
0.7%
approved 733
 
0.4%
reinstated 563
 
0.3%
suspend 193
 
0.1%
revoked 50
 
< 0.1%
Other values (4) 22
 
< 0.1%

Length

2024-08-29T13:16:21.342437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
complete 97081
48.8%
issued 83563
42.0%
filed 12045
 
6.1%
withdrawn 1754
 
0.9%
cancelled 1536
 
0.8%
expired 1370
 
0.7%
approved 733
 
0.4%
reinstated 563
 
0.3%
suspend 193
 
0.1%
revoked 50
 
< 0.1%
Other values (4) 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 297758
21.4%
s 168077
12.1%
l 112218
 
8.1%
d 101811
 
7.3%
c 100187
 
7.2%
p 100136
 
7.2%
t 99963
 
7.2%
i 99299
 
7.1%
o 97868
 
7.0%
m 97083
 
7.0%
Other values (10) 116444
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1390844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 297758
21.4%
s 168077
12.1%
l 112218
 
8.1%
d 101811
 
7.3%
c 100187
 
7.2%
p 100136
 
7.2%
t 99963
 
7.2%
i 99299
 
7.1%
o 97868
 
7.0%
m 97083
 
7.0%
Other values (10) 116444
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1390844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 297758
21.4%
s 168077
12.1%
l 112218
 
8.1%
d 101811
 
7.3%
c 100187
 
7.2%
p 100136
 
7.2%
t 99963
 
7.2%
i 99299
 
7.1%
o 97868
 
7.0%
m 97083
 
7.0%
Other values (10) 116444
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1390844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 297758
21.4%
s 168077
12.1%
l 112218
 
8.1%
d 101811
 
7.3%
c 100187
 
7.2%
p 100136
 
7.2%
t 99963
 
7.2%
i 99299
 
7.1%
o 97868
 
7.0%
m 97083
 
7.0%
Other values (10) 116444
 
8.4%
Distinct1307
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2013-01-02 00:00:00
Maximum2019-03-02 00:00:00
2024-08-29T13:16:21.503694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:21.669602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1288
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2013-01-02 00:00:00
Maximum2018-02-23 00:00:00
2024-08-29T13:16:21.886603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:22.061864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Issued Date
Date

MISSING 

Distinct1289
Distinct (%)0.7%
Missing14942
Missing (%)7.5%
Memory size1.5 MiB
Minimum2013-01-02 00:00:00
Maximum2018-02-23 00:00:00
2024-08-29T13:16:22.243863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:22.395859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Completed Date
Date

MISSING 

Distinct1300
Distinct (%)1.3%
Missing101715
Missing (%)51.1%
Memory size1.5 MiB
Minimum2013-01-04 00:00:00
Maximum2018-02-23 00:00:00
2024-08-29T13:16:22.563303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:23.322335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1288
Distinct (%)0.7%
Missing14948
Missing (%)7.5%
Memory size1.5 MiB
Minimum2013-01-02 00:00:00
Maximum2018-02-23 00:00:00
2024-08-29T13:16:23.503034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:23.663034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Structural Notification
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing191988
Missing (%)96.5%
Memory size388.6 KiB
True
 
6922
(Missing)
191988 
ValueCountFrequency (%)
True 6922
 
3.5%
(Missing) 191988
96.5%
2024-08-29T13:16:23.819038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Number of Existing Stories
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct64
Distinct (%)< 0.1%
Missing42788
Missing (%)21.5%
Infinite0
Infinite (%)0.0%
Mean5.7057077
Minimum0
Maximum78
Zeros442
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-29T13:16:23.932034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile26
Maximum78
Range78
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.6133098
Coefficient of variation (CV)1.5095953
Kurtosis11.309873
Mean5.7057077
Median Absolute Deviation (MAD)1
Skewness3.2978123
Sum890786.5
Variance74.189106
MonotonicityNot monotonic
2024-08-29T13:16:24.095318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 52770
26.5%
3 45741
23.0%
4 16055
 
8.1%
1 8793
 
4.4%
5 3767
 
1.9%
6 3696
 
1.9%
7 2359
 
1.2%
8 1732
 
0.9%
9 1264
 
0.6%
10 1129
 
0.6%
Other values (54) 18816
 
9.5%
(Missing) 42788
21.5%
ValueCountFrequency (%)
0 442
 
0.2%
1 8793
 
4.4%
1.5 1
 
< 0.1%
2 52770
26.5%
2.5 2
 
< 0.1%
3 45741
23.0%
4 16055
 
8.1%
5 3767
 
1.9%
6 3696
 
1.9%
7 2359
 
1.2%
ValueCountFrequency (%)
78 1
 
< 0.1%
63 99
< 0.1%
62 2
 
< 0.1%
61 24
 
< 0.1%
60 18
 
< 0.1%
58 93
< 0.1%
56 4
 
< 0.1%
55 14
 
< 0.1%
54 7
 
< 0.1%
53 12
 
< 0.1%

Number of Proposed Stories
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct64
Distinct (%)< 0.1%
Missing42871
Missing (%)21.6%
Infinite0
Infinite (%)0.0%
Mean5.7449644
Minimum0
Maximum78
Zeros174
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-29T13:16:24.277334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile26
Maximum78
Range78
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.6131126
Coefficient of variation (CV)1.4992456
Kurtosis11.46678
Mean5.7449644
Median Absolute Deviation (MAD)1
Skewness3.3179006
Sum896438.5
Variance74.185708
MonotonicityNot monotonic
2024-08-29T13:16:24.468338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 50994
25.6%
3 46803
23.5%
4 17594
 
8.8%
1 7893
 
4.0%
5 4044
 
2.0%
6 3792
 
1.9%
7 2318
 
1.2%
8 1703
 
0.9%
9 1334
 
0.7%
11 1120
 
0.6%
Other values (54) 18444
 
9.3%
(Missing) 42871
21.6%
ValueCountFrequency (%)
0 174
 
0.1%
1 7893
 
4.0%
1.5 1
 
< 0.1%
2 50994
25.6%
2.5 2
 
< 0.1%
3 46803
23.5%
4 17594
 
8.8%
5 4044
 
2.0%
6 3792
 
1.9%
7 2318
 
1.2%
ValueCountFrequency (%)
78 1
 
< 0.1%
63 105
0.1%
62 4
 
< 0.1%
61 24
 
< 0.1%
60 19
 
< 0.1%
58 90
< 0.1%
56 11
 
< 0.1%
55 23
 
< 0.1%
54 14
 
< 0.1%
53 12
 
< 0.1%

Voluntary Soft-Story Retrofit
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)2.9%
Missing198875
Missing (%)> 99.9%
Memory size388.6 KiB
True
 
35
(Missing)
198875 
ValueCountFrequency (%)
True 35
 
< 0.1%
(Missing) 198875
> 99.9%
2024-08-29T13:16:24.625239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Fire Only Permit
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing180082
Missing (%)90.5%
Memory size388.6 KiB
True
18828 
(Missing)
180082 
ValueCountFrequency (%)
True 18828
 
9.5%
(Missing) 180082
90.5%
2024-08-29T13:16:24.729486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2232
Distinct (%)1.5%
Missing51884
Missing (%)26.1%
Memory size1.5 MiB
Minimum2013-07-15 00:00:00
Maximum2024-02-21 00:00:00
2024-08-29T13:16:24.869392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:25.048249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estimated Cost
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct11395
Distinct (%)7.1%
Missing38068
Missing (%)19.1%
Infinite0
Infinite (%)0.0%
Mean169009.86
Minimum1
Maximum5.3795865 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-29T13:16:25.235454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13300
median11000
Q335000
95-th percentile275000
Maximum5.3795865 × 108
Range5.3795864 × 108
Interquartile range (IQR)31700

Descriptive statistics

Standard deviation3630376.9
Coefficient of variation (CV)21.480267
Kurtosis8911.3386
Mean169009.86
Median Absolute Deviation (MAD)10000
Skewness80.264355
Sum2.7183884 × 1010
Variance1.3179636 × 1013
MonotonicityNot monotonic
2024-08-29T13:16:25.423356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 17014
 
8.6%
10000 6695
 
3.4%
5000 6437
 
3.2%
20000 5702
 
2.9%
15000 4779
 
2.4%
30000 3939
 
2.0%
25000 3599
 
1.8%
3000 3569
 
1.8%
50000 3345
 
1.7%
2000 3203
 
1.6%
Other values (11385) 102560
51.6%
(Missing) 38068
 
19.1%
ValueCountFrequency (%)
1 17014
8.6%
2 1
 
< 0.1%
4 2
 
< 0.1%
5 3
 
< 0.1%
10 11
 
< 0.1%
20 5
 
< 0.1%
25 7
 
< 0.1%
30 3
 
< 0.1%
35 1
 
< 0.1%
40 1
 
< 0.1%
ValueCountFrequency (%)
537958646 1
< 0.1%
520000000 1
< 0.1%
400000000 2
< 0.1%
340000000 1
< 0.1%
272000000 1
< 0.1%
270000000 1
< 0.1%
239000000 1
< 0.1%
210000000 1
< 0.1%
175000000 1
< 0.1%
167000000 2
< 0.1%

Revised Cost
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct12629
Distinct (%)6.5%
Missing6066
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean132901.75
Minimum0
Maximum7.805 × 108
Zeros8514
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-29T13:16:25.596356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median7000
Q328715
95-th percentile227340
Maximum7.805 × 108
Range7.805 × 108
Interquartile range (IQR)28714

Descriptive statistics

Standard deviation3584878.7
Coefficient of variation (CV)26.973901
Kurtosis17049.467
Mean132901.75
Median Absolute Deviation (MAD)6999
Skewness108.7016
Sum2.5629306 × 1010
Variance1.2851355 × 1013
MonotonicityNot monotonic
2024-08-29T13:16:25.781398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 47229
23.7%
0 8514
 
4.3%
10000 5474
 
2.8%
5000 5453
 
2.7%
20000 4696
 
2.4%
15000 4001
 
2.0%
30000 3291
 
1.7%
25000 3135
 
1.6%
3000 3067
 
1.5%
2000 2955
 
1.5%
Other values (12619) 105029
52.8%
(Missing) 6066
 
3.0%
ValueCountFrequency (%)
0 8514
 
4.3%
0.01 1
 
< 0.1%
1 47229
23.7%
1.2 1
 
< 0.1%
2 4
 
< 0.1%
5 3
 
< 0.1%
10 11
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
14 6
 
< 0.1%
ValueCountFrequency (%)
780500000 1
 
< 0.1%
520000000 1
 
< 0.1%
400000000 2
< 0.1%
336200000 1
 
< 0.1%
270000000 1
 
< 0.1%
266061486 1
 
< 0.1%
239000000 1
 
< 0.1%
210000000 1
 
< 0.1%
207680000 1
 
< 0.1%
200000000 4
< 0.1%

Existing Use
Text

MISSING 

Distinct93
Distinct (%)0.1%
Missing41117
Missing (%)20.7%
Memory size11.8 MiB
2024-08-29T13:16:26.308729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length19
Mean length13.170286
Min length4

Characters and Unicode

Total characters2078179
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowapartments
2nd rowapartments
3rd row2 family dwelling
4th row2 family dwelling
5th row1 family dwelling
ValueCountFrequency (%)
family 67756
21.4%
dwelling 67756
21.4%
1 46768
14.8%
apartments 40800
12.9%
office 24617
 
7.8%
2 20988
 
6.6%
sales 7032
 
2.2%
retail 6910
 
2.2%
hndlng 4886
 
1.5%
food/beverage 4886
 
1.5%
Other values (144) 24040
 
7.6%
2024-08-29T13:16:27.038677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 235150
 
11.3%
a 181738
 
8.7%
i 179803
 
8.7%
e 177532
 
8.5%
158646
 
7.6%
n 129792
 
6.2%
f 123927
 
6.0%
m 113200
 
5.4%
t 108688
 
5.2%
d 81432
 
3.9%
Other values (29) 588271
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2078179
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 235150
 
11.3%
a 181738
 
8.7%
i 179803
 
8.7%
e 177532
 
8.5%
158646
 
7.6%
n 129792
 
6.2%
f 123927
 
6.0%
m 113200
 
5.4%
t 108688
 
5.2%
d 81432
 
3.9%
Other values (29) 588271
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2078179
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 235150
 
11.3%
a 181738
 
8.7%
i 179803
 
8.7%
e 177532
 
8.5%
158646
 
7.6%
n 129792
 
6.2%
f 123927
 
6.0%
m 113200
 
5.4%
t 108688
 
5.2%
d 81432
 
3.9%
Other values (29) 588271
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2078179
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 235150
 
11.3%
a 181738
 
8.7%
i 179803
 
8.7%
e 177532
 
8.5%
158646
 
7.6%
n 129792
 
6.2%
f 123927
 
6.0%
m 113200
 
5.4%
t 108688
 
5.2%
d 81432
 
3.9%
Other values (29) 588271
28.3%

Existing Units
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct348
Distinct (%)0.2%
Missing51543
Missing (%)25.9%
Infinite0
Infinite (%)0.0%
Mean15.666006
Minimum0
Maximum1907
Zeros29135
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-29T13:16:27.231825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q34
95-th percentile64
Maximum1907
Range1907
Interquartile range (IQR)3

Descriptive statistics

Standard deviation74.47512
Coefficient of variation (CV)4.7539316
Kurtosis238.3122
Mean15.666006
Median Absolute Deviation (MAD)1
Skewness12.867436
Sum2308652.3
Variance5546.5435
MonotonicityNot monotonic
2024-08-29T13:16:27.416974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 47347
23.8%
0 29135
14.6%
2 21805
11.0%
3 8616
 
4.3%
6 6066
 
3.0%
4 5364
 
2.7%
12 2535
 
1.3%
5 2347
 
1.2%
8 1728
 
0.9%
9 1188
 
0.6%
Other values (338) 21236
10.7%
(Missing) 51543
25.9%
ValueCountFrequency (%)
0 29135
14.6%
0.3 1
 
< 0.1%
1 47347
23.8%
2 21805
11.0%
3 8616
 
4.3%
4 5364
 
2.7%
5 2347
 
1.2%
6 6066
 
3.0%
7 1172
 
0.6%
8 1728
 
0.9%
ValueCountFrequency (%)
1907 44
< 0.1%
1732 8
 
< 0.1%
1500 51
< 0.1%
1499 1
 
< 0.1%
1186 51
< 0.1%
1010 2
 
< 0.1%
1005 21
< 0.1%
1004 2
 
< 0.1%
1000 1
 
< 0.1%
948 1
 
< 0.1%

Proposed Use
Text

MISSING 

Distinct94
Distinct (%)0.1%
Missing42441
Missing (%)21.3%
Memory size11.8 MiB
2024-08-29T13:16:28.073260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length19
Mean length13.208827
Min length4

Characters and Unicode

Total characters2066772
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowapartments
2nd rowapartments
3rd row2 family dwelling
4th row2 family dwelling
5th row1 family dwelling
ValueCountFrequency (%)
family 68410
21.8%
dwelling 68410
21.8%
1 46348
14.8%
apartments 43035
13.7%
office 23963
 
7.7%
2 22062
 
7.0%
sales 5193
 
1.7%
retail 5079
 
1.6%
food/beverage 5053
 
1.6%
hndlng 5053
 
1.6%
Other values (146) 20614
 
6.6%
2024-08-29T13:16:29.070822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 232125
 
11.2%
a 179985
 
8.7%
i 177628
 
8.6%
e 175966
 
8.5%
156751
 
7.6%
n 130398
 
6.3%
f 123053
 
6.0%
m 116197
 
5.6%
t 107434
 
5.2%
d 82574
 
4.0%
Other values (29) 584661
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2066772
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 232125
 
11.2%
a 179985
 
8.7%
i 177628
 
8.6%
e 175966
 
8.5%
156751
 
7.6%
n 130398
 
6.3%
f 123053
 
6.0%
m 116197
 
5.6%
t 107434
 
5.2%
d 82574
 
4.0%
Other values (29) 584661
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2066772
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 232125
 
11.2%
a 179985
 
8.7%
i 177628
 
8.6%
e 175966
 
8.5%
156751
 
7.6%
n 130398
 
6.3%
f 123053
 
6.0%
m 116197
 
5.6%
t 107434
 
5.2%
d 82574
 
4.0%
Other values (29) 584661
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2066772
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 232125
 
11.2%
a 179985
 
8.7%
i 177628
 
8.6%
e 175966
 
8.5%
156751
 
7.6%
n 130398
 
6.3%
f 123053
 
6.0%
m 116197
 
5.6%
t 107434
 
5.2%
d 82574
 
4.0%
Other values (29) 584661
28.3%

Proposed Units
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct368
Distinct (%)0.2%
Missing50915
Missing (%)25.6%
Infinite0
Infinite (%)0.0%
Mean16.510882
Minimum0
Maximum1911
Zeros26885
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-29T13:16:29.267877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile70
Maximum1911
Range1911
Interquartile range (IQR)3

Descriptive statistics

Standard deviation75.218997
Coefficient of variation (CV)4.5557225
Kurtosis223.71262
Mean16.510882
Median Absolute Deviation (MAD)1
Skewness12.330487
Sum2443528
Variance5657.8975
MonotonicityNot monotonic
2024-08-29T13:16:29.451071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 46914
23.6%
0 26885
13.5%
2 22892
11.5%
3 9347
 
4.7%
6 5872
 
3.0%
4 5504
 
2.8%
12 2474
 
1.2%
5 2400
 
1.2%
8 1800
 
0.9%
7 1336
 
0.7%
Other values (358) 22571
11.3%
(Missing) 50915
25.6%
ValueCountFrequency (%)
0 26885
13.5%
1 46914
23.6%
2 22892
11.5%
3 9347
 
4.7%
4 5504
 
2.8%
5 2400
 
1.2%
6 5872
 
3.0%
7 1336
 
0.7%
8 1800
 
0.9%
9 1270
 
0.6%
ValueCountFrequency (%)
1911 1
 
< 0.1%
1907 43
< 0.1%
1732 8
 
< 0.1%
1500 48
< 0.1%
1499 1
 
< 0.1%
1199 1
 
< 0.1%
1186 47
< 0.1%
1014 4
 
< 0.1%
1010 2
 
< 0.1%
1005 17
 
< 0.1%

Plansets
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing37311
Missing (%)18.8%
Infinite0
Infinite (%)0.0%
Mean1.274649
Minimum0
Maximum9000
Zeros63247
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-29T13:16:29.604126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q32
95-th percentile2
Maximum9000
Range9000
Interquartile range (IQR)2

Descriptive statistics

Standard deviation22.406792
Coefficient of variation (CV)17.578794
Kurtosis160982
Mean1.274649
Median Absolute Deviation (MAD)0
Skewness400.84207
Sum205982
Variance502.06432
MonotonicityNot monotonic
2024-08-29T13:16:29.715028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 98093
49.3%
0 63247
31.8%
3 250
 
0.1%
4 3
 
< 0.1%
6 2
 
< 0.1%
1 2
 
< 0.1%
20 1
 
< 0.1%
9000 1
 
< 0.1%
(Missing) 37311
 
18.8%
ValueCountFrequency (%)
0 63247
31.8%
1 2
 
< 0.1%
2 98093
49.3%
3 250
 
0.1%
4 3
 
< 0.1%
6 2
 
< 0.1%
20 1
 
< 0.1%
9000 1
 
< 0.1%
ValueCountFrequency (%)
9000 1
 
< 0.1%
20 1
 
< 0.1%
6 2
 
< 0.1%
4 3
 
< 0.1%
3 250
 
0.1%
2 98093
49.3%
1 2
 
< 0.1%
0 63247
31.8%

TIDF Compliance
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing198908
Missing (%)> 99.9%
Memory size6.1 MiB
2024-08-29T13:16:29.807365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowP
2nd rowY
ValueCountFrequency (%)
p 1
50.0%
y 1
50.0%
2024-08-29T13:16:30.040771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 1
50.0%
Y 1
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 1
50.0%
Y 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 1
50.0%
Y 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 1
50.0%
Y 1
50.0%

Existing Construction Type
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct7
Distinct (%)< 0.1%
Missing43369
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean4.7156312
Minimum-99999
Maximum99999
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size1.5 MiB
2024-08-29T13:16:30.206759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-99999
5-th percentile1
Q13
median5
Q35
95-th percentile5
Maximum99999
Range199998
Interquartile range (IQR)2

Descriptive statistics

Standard deviation566.96662
Coefficient of variation (CV)120.23133
Kurtosis31105.634
Mean4.7156312
Median Absolute Deviation (MAD)0
Skewness35.250938
Sum733474
Variance321451.15
MonotonicityNot monotonic
2024-08-29T13:16:30.331651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 113350
57.0%
1 28073
 
14.1%
3 9664
 
4.9%
2 4068
 
2.0%
4 381
 
0.2%
99999 3
 
< 0.1%
-99999 2
 
< 0.1%
(Missing) 43369
 
21.8%
ValueCountFrequency (%)
-99999 2
 
< 0.1%
1 28073
 
14.1%
2 4068
 
2.0%
3 9664
 
4.9%
4 381
 
0.2%
5 113350
57.0%
99999 3
 
< 0.1%
ValueCountFrequency (%)
99999 3
 
< 0.1%
5 113350
57.0%
4 381
 
0.2%
3 9664
 
4.9%
2 4068
 
2.0%
1 28073
 
14.1%
-99999 2
 
< 0.1%

Existing Construction Type Description
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing43370
Missing (%)21.8%
Memory size13.1 MiB
wood frame (5)
113350 
constr type 1
28072 
constr type 3
 
9663
constr type 2
 
4068
constr type 4
 
381
Other values (3)
 
6

Length

Max length16
Median length14
Mean length13.728854
Min length13

Characters and Unicode

Total characters2135386
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowwood frame (5)
2nd rowwood frame (5)
3rd rowwood frame (5)
4th rowwood frame (5)
5th rowwood frame (5)

Common Values

ValueCountFrequency (%)
wood frame (5) 113350
57.0%
constr type 1 28072
 
14.1%
constr type 3 9663
 
4.9%
constr type 2 4068
 
2.0%
constr type 4 381
 
0.2%
wood frame (5) 4
 
< 0.1%
constr type 1 1
 
< 0.1%
constr type 3 1
 
< 0.1%
(Missing) 43370
 
21.8%

Length

2024-08-29T13:16:30.478746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:16:30.655651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
wood 113354
24.3%
frame 113354
24.3%
5 113354
24.3%
constr 42186
 
9.0%
type 42186
 
9.0%
1 28073
 
6.0%
3 9664
 
2.1%
2 4068
 
0.9%
4 381
 
0.1%

Most occurring characters

ValueCountFrequency (%)
311092
14.6%
o 268894
12.6%
e 155540
 
7.3%
r 155540
 
7.3%
w 113354
 
5.3%
5 113354
 
5.3%
( 113354
 
5.3%
) 113354
 
5.3%
m 113354
 
5.3%
a 113354
 
5.3%
Other values (12) 564196
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2135386
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
311092
14.6%
o 268894
12.6%
e 155540
 
7.3%
r 155540
 
7.3%
w 113354
 
5.3%
5 113354
 
5.3%
( 113354
 
5.3%
) 113354
 
5.3%
m 113354
 
5.3%
a 113354
 
5.3%
Other values (12) 564196
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2135386
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
311092
14.6%
o 268894
12.6%
e 155540
 
7.3%
r 155540
 
7.3%
w 113354
 
5.3%
5 113354
 
5.3%
( 113354
 
5.3%
) 113354
 
5.3%
m 113354
 
5.3%
a 113354
 
5.3%
Other values (12) 564196
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2135386
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
311092
14.6%
o 268894
12.6%
e 155540
 
7.3%
r 155540
 
7.3%
w 113354
 
5.3%
5 113354
 
5.3%
( 113354
 
5.3%
) 113354
 
5.3%
m 113354
 
5.3%
a 113354
 
5.3%
Other values (12) 564196
26.4%

Proposed Construction Type
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing43165
Missing (%)21.7%
Memory size11.5 MiB
5.0
114386 
1.0
27843 
3.0
 
9361
2.0
 
3778
4.0
 
377

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters467235
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 114386
57.5%
1.0 27843
 
14.0%
3.0 9361
 
4.7%
2.0 3778
 
1.9%
4.0 377
 
0.2%
(Missing) 43165
 
21.7%

Length

2024-08-29T13:16:30.805735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:16:30.953657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0 114386
73.4%
1.0 27843
 
17.9%
3.0 9361
 
6.0%
2.0 3778
 
2.4%
4.0 377
 
0.2%

Most occurring characters

ValueCountFrequency (%)
. 155745
33.3%
0 155745
33.3%
5 114386
24.5%
1 27843
 
6.0%
3 9361
 
2.0%
2 3778
 
0.8%
4 377
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 467235
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 155745
33.3%
0 155745
33.3%
5 114386
24.5%
1 27843
 
6.0%
3 9361
 
2.0%
2 3778
 
0.8%
4 377
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 467235
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 155745
33.3%
0 155745
33.3%
5 114386
24.5%
1 27843
 
6.0%
3 9361
 
2.0%
2 3778
 
0.8%
4 377
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 467235
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 155745
33.3%
0 155745
33.3%
5 114386
24.5%
1 27843
 
6.0%
3 9361
 
2.0%
2 3778
 
0.8%
4 377
 
0.1%

Proposed Construction Type Description
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing43165
Missing (%)21.7%
Memory size13.1 MiB
wood frame (5)
114386 
constr type 1
27843 
constr type 3
 
9361
constr type 2
 
3778
constr type 4
 
377

Length

Max length14
Median length14
Mean length13.734444
Min length13

Characters and Unicode

Total characters2139071
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwood frame (5)
2nd rowwood frame (5)
3rd rowwood frame (5)
4th rowwood frame (5)
5th rowwood frame (5)

Common Values

ValueCountFrequency (%)
wood frame (5) 114386
57.5%
constr type 1 27843
 
14.0%
constr type 3 9361
 
4.7%
constr type 2 3778
 
1.9%
constr type 4 377
 
0.2%
(Missing) 43165
 
21.7%

Length

2024-08-29T13:16:31.096175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:16:31.258754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
wood 114386
24.5%
frame 114386
24.5%
5 114386
24.5%
constr 41359
 
8.9%
type 41359
 
8.9%
1 27843
 
6.0%
3 9361
 
2.0%
2 3778
 
0.8%
4 377
 
0.1%

Most occurring characters

ValueCountFrequency (%)
311490
14.6%
o 270131
12.6%
e 155745
 
7.3%
r 155745
 
7.3%
w 114386
 
5.3%
5 114386
 
5.3%
( 114386
 
5.3%
) 114386
 
5.3%
m 114386
 
5.3%
a 114386
 
5.3%
Other values (12) 559644
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2139071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
311490
14.6%
o 270131
12.6%
e 155745
 
7.3%
r 155745
 
7.3%
w 114386
 
5.3%
5 114386
 
5.3%
( 114386
 
5.3%
) 114386
 
5.3%
m 114386
 
5.3%
a 114386
 
5.3%
Other values (12) 559644
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2139071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
311490
14.6%
o 270131
12.6%
e 155745
 
7.3%
r 155745
 
7.3%
w 114386
 
5.3%
5 114386
 
5.3%
( 114386
 
5.3%
) 114386
 
5.3%
m 114386
 
5.3%
a 114386
 
5.3%
Other values (12) 559644
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2139071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
311490
14.6%
o 270131
12.6%
e 155745
 
7.3%
r 155745
 
7.3%
w 114386
 
5.3%
5 114386
 
5.3%
( 114386
 
5.3%
) 114386
 
5.3%
m 114386
 
5.3%
a 114386
 
5.3%
Other values (12) 559644
26.2%

Site Permit
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing193550
Missing (%)97.3%
Memory size388.6 KiB
True
 
5360
(Missing)
193550 
ValueCountFrequency (%)
True 5360
 
2.7%
(Missing) 193550
97.3%
2024-08-29T13:16:31.416763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Supervisor District
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)< 0.1%
Missing1719
Missing (%)0.9%
Memory size11.0 MiB
3
28649 
8
26761 
2
25484 
6
24797 
5
19045 
Other values (9)
72455 

Length

Max length6
Median length1
Mean length1.0968959
Min length1

Characters and Unicode

Total characters216298
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row8
2nd row2
3rd row9
4th row1
5th row9

Common Values

ValueCountFrequency (%)
3 28649
14.4%
8 26761
13.5%
2 25484
12.8%
6 24797
12.5%
5 19045
9.6%
9 16362
8.2%
7 14365
7.2%
1 13039
6.6%
10 12153
6.1%
4 9592
 
4.8%
Other values (4) 6944
 
3.5%

Length

2024-08-29T13:16:31.512846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3 28649
14.5%
8 26761
13.6%
2 25484
12.9%
6 24797
12.6%
5 19045
9.7%
9 16362
8.3%
7 14365
7.3%
1 13039
6.6%
10 12153
6.2%
4 9592
 
4.9%
Other values (4) 6944
 
3.5%

Most occurring characters

ValueCountFrequency (%)
1 39074
18.1%
3 28649
13.2%
8 26761
12.4%
2 25484
11.8%
6 24797
11.5%
5 19045
8.8%
9 16362
7.6%
7 14365
 
6.6%
0 12153
 
5.6%
4 9592
 
4.4%
Other values (10) 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 216298
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 39074
18.1%
3 28649
13.2%
8 26761
12.4%
2 25484
11.8%
6 24797
11.5%
5 19045
8.8%
9 16362
7.6%
7 14365
 
6.6%
0 12153
 
5.6%
4 9592
 
4.4%
Other values (10) 16
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 216298
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 39074
18.1%
3 28649
13.2%
8 26761
12.4%
2 25484
11.8%
6 24797
11.5%
5 19045
8.8%
9 16362
7.6%
7 14365
 
6.6%
0 12153
 
5.6%
4 9592
 
4.4%
Other values (10) 16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 216298
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 39074
18.1%
3 28649
13.2%
8 26761
12.4%
2 25484
11.8%
6 24797
11.5%
5 19045
8.8%
9 16362
7.6%
7 14365
 
6.6%
0 12153
 
5.6%
4 9592
 
4.4%
Other values (10) 16
 
< 0.1%

Neighborhoods - Analysis Boundaries
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)< 0.1%
Missing1725
Missing (%)0.9%
Memory size13.6 MiB
Financial District/South Beach
21816 
Mission
14682 
Sunset/Parkside
 
10207
West of Twin Peaks
 
8740
Castro/Upper Market
 
8527
Other values (36)
133213 

Length

Max length30
Median length18
Mean length14.780977
Min length6

Characters and Unicode

Total characters2914587
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMission
2nd rowPacific Heights
3rd rowMission
4th rowOuter Richmond
5th rowBernal Heights

Common Values

ValueCountFrequency (%)
Financial District/South Beach 21816
 
11.0%
Mission 14682
 
7.4%
Sunset/Parkside 10207
 
5.1%
West of Twin Peaks 8740
 
4.4%
Castro/Upper Market 8527
 
4.3%
Pacific Heights 8508
 
4.3%
Marina 8244
 
4.1%
Outer Richmond 7855
 
3.9%
Noe Valley 7844
 
3.9%
South of Market 7573
 
3.8%
Other values (31) 93189
46.8%

Length

2024-08-29T13:16:31.656754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
beach 25870
 
6.5%
financial 21816
 
5.5%
district/south 21816
 
5.5%
mission 19211
 
4.9%
heights 18661
 
4.7%
of 16313
 
4.1%
market 16100
 
4.1%
hill 15798
 
4.0%
valley 14233
 
3.6%
richmond 12313
 
3.1%
Other values (46) 212950
53.9%

Most occurring characters

ValueCountFrequency (%)
i 281035
 
9.6%
e 234500
 
8.0%
a 203693
 
7.0%
n 198634
 
6.8%
197896
 
6.8%
t 194569
 
6.7%
s 176846
 
6.1%
o 150144
 
5.2%
r 148187
 
5.1%
c 109199
 
3.7%
Other values (36) 1019884
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2914587
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 281035
 
9.6%
e 234500
 
8.0%
a 203693
 
7.0%
n 198634
 
6.8%
197896
 
6.8%
t 194569
 
6.7%
s 176846
 
6.1%
o 150144
 
5.2%
r 148187
 
5.1%
c 109199
 
3.7%
Other values (36) 1019884
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2914587
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 281035
 
9.6%
e 234500
 
8.0%
a 203693
 
7.0%
n 198634
 
6.8%
197896
 
6.8%
t 194569
 
6.7%
s 176846
 
6.1%
o 150144
 
5.2%
r 148187
 
5.1%
c 109199
 
3.7%
Other values (36) 1019884
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2914587
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 281035
 
9.6%
e 234500
 
8.0%
a 203693
 
7.0%
n 198634
 
6.8%
197896
 
6.8%
t 194569
 
6.7%
s 176846
 
6.1%
o 150144
 
5.2%
r 148187
 
5.1%
c 109199
 
3.7%
Other values (36) 1019884
35.0%

Zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)< 0.1%
Missing1716
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean94115.501
Minimum94102
Maximum94158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-29T13:16:31.807759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum94102
5-th percentile94103
Q194109
median94114
Q394122
95-th percentile94133
Maximum94158
Range56
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2701045
Coefficient of variation (CV)9.8497107 × 10-5
Kurtosis1.2263383
Mean94115.501
Median Absolute Deviation (MAD)6
Skewness0.85171252
Sum1.8559012 × 1010
Variance85.934838
MonotonicityNot monotonic
2024-08-29T13:16:31.937753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
94110 17838
 
9.0%
94114 13404
 
6.7%
94117 11781
 
5.9%
94109 11348
 
5.7%
94103 10988
 
5.5%
94115 10095
 
5.1%
94118 9813
 
4.9%
94123 9515
 
4.8%
94122 8886
 
4.5%
94105 8628
 
4.3%
Other values (17) 84898
42.7%
ValueCountFrequency (%)
94102 7164
3.6%
94103 10988
5.5%
94104 4229
 
2.1%
94105 8628
4.3%
94107 7706
3.9%
94108 5321
 
2.7%
94109 11348
5.7%
94110 17838
9.0%
94111 5385
 
2.7%
94112 7898
4.0%
ValueCountFrequency (%)
94158 1058
 
0.5%
94134 2983
 
1.5%
94133 7424
3.7%
94132 3507
 
1.8%
94131 7665
3.9%
94130 81
 
< 0.1%
94129 23
 
< 0.1%
94127 4993
2.5%
94124 5266
2.6%
94123 9515
4.8%
Distinct57604
Distinct (%)29.2%
Missing1700
Missing (%)0.9%
Memory size18.3 MiB
2024-08-29T13:16:32.532237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length41
Median length40
Mean length40.044278
Min length35

Characters and Unicode

Total characters7897132
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26164 ?
Unique (%)13.3%

Sample

1st row(37.765906581390105, -122.42453043666066)
2nd row(37.78788490521045, -122.43641930200963)
3rd row(37.75241643936911, -122.40877419451499)
4th row(37.77928233382438, -122.48608415176169)
5th row(37.74224320174262, -122.41981352499106)
ValueCountFrequency (%)
37.79226164705184 554
 
0.1%
122.4034859571375 554
 
0.1%
37.79294896659241 330
 
0.1%
122.39809861435491 330
 
0.1%
37.728556952954136 281
 
0.1%
122.47676641508518 281
 
0.1%
37.77523036414975 276
 
0.1%
122.4174703200545 276
 
0.1%
37.78977799888473 252
 
0.1%
122.40173648131338 252
 
0.1%
Other values (115177) 391034
99.1%
2024-08-29T13:16:33.278222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 899264
11.4%
7 893479
11.3%
3 736584
9.3%
1 703929
8.9%
4 698318
8.8%
5 534319
 
6.8%
6 524243
 
6.6%
8 524195
 
6.6%
9 523487
 
6.6%
0 478844
 
6.1%
Other values (6) 1380470
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7897132
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 899264
11.4%
7 893479
11.3%
3 736584
9.3%
1 703929
8.9%
4 698318
8.8%
5 534319
 
6.8%
6 524243
 
6.6%
8 524195
 
6.6%
9 523487
 
6.6%
0 478844
 
6.1%
Other values (6) 1380470
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7897132
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 899264
11.4%
7 893479
11.3%
3 736584
9.3%
1 703929
8.9%
4 698318
8.8%
5 534319
 
6.8%
6 524243
 
6.6%
8 524195
 
6.6%
9 523487
 
6.6%
0 478844
 
6.1%
Other values (6) 1380470
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7897132
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 899264
11.4%
7 893479
11.3%
3 736584
9.3%
1 703929
8.9%
4 698318
8.8%
5 534319
 
6.8%
6 524243
 
6.6%
8 524195
 
6.6%
9 523487
 
6.6%
0 478844
 
6.1%
Other values (6) 1380470
17.5%

Record ID
Real number (ℝ)

Distinct38347
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1620464 × 1012
Minimum1.2935322 × 1010
Maximum1.49834 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-29T13:16:33.452675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.2935322 × 1010
5-th percentile1.34534 × 1011
Q11.30857 × 1012
median1.37184 × 1012
Q31.435 × 1012
95-th percentile1.4859055 × 1012
Maximum1.49834 × 1012
Range1.4854047 × 1012
Interquartile range (IQR)1.2643 × 1011

Descriptive statistics

Standard deviation4.9182207 × 1011
Coefficient of variation (CV)0.42323788
Kurtosis0.55165922
Mean1.1620464 × 1012
Median Absolute Deviation (MAD)6.322 × 1010
Skewness-1.5726861
Sum2.3114266 × 1017
Variance2.4188895 × 1023
MonotonicityNot monotonic
2024-08-29T13:16:33.606684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.41585 × 101211
 
< 0.1%
1.34437 × 101211
 
< 0.1%
1.30223 × 101210
 
< 0.1%
1.44093 × 101210
 
< 0.1%
1.45828 × 101210
 
< 0.1%
1.45923 × 101210
 
< 0.1%
1.40362 × 101210
 
< 0.1%
1.39947 × 101210
 
< 0.1%
1.36825 × 101210
 
< 0.1%
1.36952 × 101210
 
< 0.1%
Other values (38337) 198808
99.9%
ValueCountFrequency (%)
1.29353215 × 10101
< 0.1%
1.295722223 × 10101
< 0.1%
1.296226196 × 10101
< 0.1%
1.296342101 × 10101
< 0.1%
1.296629102 × 10101
< 0.1%
1.297286249 × 10101
< 0.1%
1.297668221 × 10101
< 0.1%
1.297846157 × 10101
< 0.1%
1.298230162 × 10101
< 0.1%
1.298784182 × 10101
< 0.1%
ValueCountFrequency (%)
1.49834 × 10127
< 0.1%
1.49833 × 10127
< 0.1%
1.49832 × 10127
< 0.1%
1.49831 × 10128
< 0.1%
1.4983 × 10128
< 0.1%
1.49829 × 10129
< 0.1%
1.49828 × 10127
< 0.1%
1.49827 × 10125
< 0.1%
1.49826 × 10125
< 0.1%
1.49825 × 10129
< 0.1%

Interactions

2024-08-29T13:16:02.000856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:35.961122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:38.035474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:40.091926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:41.931278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:44.056361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:46.638925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:48.725181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:50.764332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:52.897709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:55.147049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:57.167629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:59.887236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:02.170937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:36.122226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:38.237055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:40.246916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:42.083361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:44.230353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:46.796931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:48.878175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:50.935252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:53.048244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:55.326857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:57.339637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:00.059739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:02.291857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:36.248534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:38.386432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:40.383382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:42.206357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:44.373362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:46.933950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:48.997191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:51.076759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:53.197434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:55.455861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:57.463634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:00.175752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:02.458860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:36.398565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:38.521805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:40.548279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:42.373360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:44.530381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:47.086929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:49.137130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:51.238666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:53.355693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:55.616469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:57.611678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:00.334820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:02.652856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:36.566516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:38.699232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:40.670388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:42.532364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:45.099359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:47.282957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:49.311179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:51.406752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:53.538045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:55.794730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:58.267572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:00.497850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:02.814939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:36.729525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:38.855541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:40.801354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:42.724368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:45.297857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:47.456851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:49.473168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:51.565664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:53.749406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:55.955779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:58.456572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:00.646736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:02.971873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:36.898860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:39.006698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:40.932351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:42.890351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:45.493938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:47.600180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:49.628177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:51.711038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:53.916873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:56.107793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:58.620663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:00.791748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:03.126189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:37.057996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:39.174687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:41.083365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:43.044319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:45.668924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:47.755101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:49.787360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:51.865962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:54.090831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:56.260474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:58.807648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:00.964753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:03.313276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:37.209575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:39.327689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:41.207383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:43.214356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:45.831941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:47.909200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:49.937251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:52.044559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:54.273824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:56.419834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:58.985650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:01.154779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:03.483257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:37.373552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:39.488467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:41.334376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:43.409282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:46.009852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:48.067186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:50.086254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:52.229650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:54.434824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:56.574805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:59.165679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:01.322872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:03.636262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:37.528452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:39.627523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:41.497368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:43.556374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:46.151931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:48.220177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:50.239250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:52.378553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:54.585296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:56.711730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:59.376107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:01.484784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:03.819170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:37.696083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:39.798833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:41.646371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:43.722350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:46.328927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:48.390203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:50.432323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:52.555233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:54.783694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:56.861310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:59.533020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:01.666868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:03.982649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:37.859980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:39.954917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:41.772375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:43.875387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:46.471931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:48.563196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:50.594249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:52.722334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:54.954831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:57.010325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:15:59.721013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:16:01.817157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-08-29T13:16:33.772784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Current StatusEstimated CostExisting Construction TypeExisting Construction Type DescriptionExisting UnitsNeighborhoods - Analysis BoundariesNumber of Existing StoriesNumber of Proposed StoriesPermit TypePermit Type DefinitionPlansetsProposed Construction TypeProposed Construction Type DescriptionProposed UnitsRecord IDRevised CostStreet NumberStreet Number SuffixStreet SuffixSupervisor DistrictUnitZipcode
Current Status1.0000.0150.0000.0280.0000.0390.0230.0230.1350.1030.0000.0320.0320.0020.1060.0150.0180.1730.0260.0270.0000.027
Estimated Cost0.0151.000-0.0730.0000.0090.0090.0990.117-0.2080.1490.2590.0210.0210.0080.0250.884-0.0200.0000.0000.0120.006-0.030
Existing Construction Type0.000-0.0731.0000.0000.2630.000-0.620-0.6240.0060.000-0.3460.0000.0000.2520.091-0.0810.2211.0000.0000.000-0.1450.487
Existing Construction Type Description0.0280.0000.0001.0000.1150.3260.3070.3110.0700.4010.0000.9940.9940.1150.0850.0000.0940.0730.1110.2690.1180.248
Existing Units0.0000.0090.2630.1151.0000.1310.1850.1680.0050.007-0.1690.1420.1420.9810.052-0.0040.1670.0000.0630.0820.4380.049
Neighborhoods - Analysis Boundaries0.0390.0090.0000.3260.1311.0000.2750.2780.0930.0710.0000.4260.4260.1320.3680.0080.3220.2240.2110.7800.1200.770
Number of Existing Stories0.0230.099-0.6200.3070.1850.2751.0000.9820.0250.0300.2860.4100.4100.181-0.1090.099-0.1330.0790.0920.2150.197-0.411
Number of Proposed Stories0.0230.117-0.6240.3110.1680.2780.9821.000-0.0210.0310.3110.4090.4090.194-0.1060.110-0.1280.0680.0920.2170.196-0.413
Permit Type0.135-0.2080.0060.0700.0050.0930.025-0.0211.0001.000-0.2850.0570.057-0.069-0.013-0.243-0.0050.2040.0580.0570.0640.001
Permit Type Definition0.1030.1490.0000.4010.0070.0710.0300.0311.0001.0000.0000.0490.0490.0150.0230.1620.0150.2040.0440.0430.0000.043
Plansets0.0000.259-0.3460.000-0.1690.0000.2860.311-0.2850.0001.0000.0000.000-0.133-0.0150.233-0.0641.0000.0000.003-0.189-0.204
Proposed Construction Type0.0320.0210.0000.9940.1420.4260.4100.4090.0570.0490.0001.0001.0000.1430.0850.0170.1210.0540.1480.3560.1330.306
Proposed Construction Type Description0.0320.0210.0000.9940.1420.4260.4100.4090.0570.0490.0001.0001.0000.1430.0850.0170.1210.0540.1480.3560.1330.306
Proposed Units0.0020.0080.2520.1150.9810.1320.1810.194-0.0690.015-0.1330.1430.1431.0000.053-0.0070.1680.0000.0640.0870.4370.029
Record ID0.1060.0250.0910.0850.0520.368-0.109-0.106-0.0130.023-0.0150.0850.0850.0531.000-0.015-0.0230.2840.0940.378-0.230-0.003
Revised Cost0.0150.884-0.0810.000-0.0040.0080.0990.110-0.2430.1620.2330.0170.017-0.007-0.0151.000-0.0630.0000.0000.0100.024-0.029
Street Number0.018-0.0200.2210.0940.1670.322-0.133-0.128-0.0050.015-0.0640.1210.1210.168-0.023-0.0631.0000.1040.1190.240-0.0050.151
Street Number Suffix0.1730.0001.0000.0730.0000.2240.0790.0680.2040.2041.0000.0540.0540.0000.2840.0000.1041.0000.0780.1980.0000.195
Street Suffix0.0260.0000.0000.1110.0630.2110.0920.0920.0580.0440.0000.1480.1480.0640.0940.0000.1190.0781.0000.2150.0790.247
Supervisor District0.0270.0120.0000.2690.0820.7800.2150.2170.0570.0430.0030.3560.3560.0870.3780.0100.2400.1980.2151.0000.0650.537
Unit0.0000.006-0.1450.1180.4380.1200.1970.1960.0640.000-0.1890.1330.1330.437-0.2300.024-0.0050.0000.0790.0651.000-0.060
Zipcode0.027-0.0300.4870.2480.0490.770-0.411-0.4130.0010.043-0.2040.3060.3060.029-0.003-0.0290.1510.1950.2470.537-0.0601.000

Missing values

2024-08-29T13:16:04.480545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-29T13:16:05.722240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-29T13:16:08.540074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Permit NumberPermit TypePermit Type DefinitionPermit Creation DateBlockLotStreet NumberStreet Number SuffixStreet NameStreet SuffixUnitUnit SuffixDescriptionCurrent StatusCurrent Status DateFiled DateIssued DateCompleted DateFirst Construction Document DateStructural NotificationNumber of Existing StoriesNumber of Proposed StoriesVoluntary Soft-Story RetrofitFire Only PermitPermit Expiration DateEstimated CostRevised CostExisting UseExisting UnitsProposed UseProposed UnitsPlansetsTIDF ComplianceExisting Construction TypeExisting Construction Type DescriptionProposed Construction TypeProposed Construction Type DescriptionSite PermitSupervisor DistrictNeighborhoods - Analysis BoundariesZipcodeLocationRecord ID
0M3944678otc alterations permit5/15/20133751172300003rDStNaNNaNstreet spaceissued5/15/20135/15/20135/15/2013NaN5/15/2013NaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.304810e+12
12.01505E+118otc alterations permit5/22/201535566328AgUeRrErOStNaNNaNremodel (e) bathroom, convert (e) bedrm into bed room and office, all work at ground floor, comply with complaint 200452633. items 3 & 4: existing furnaces to be used.complete12/18/20155/22/20156/10/201512/18/20156/10/2015NaN3.03.0NaNNaN6/4/201618000.018000.0apartments3.0apartments3.02.0NaN5.0wood frame (5)5.0wood frame (5)NaN8Mission94103.0(37.765906581390105, -122.42453043666066)1.382530e+12
2M7982478otc alterations permit6/16/2017655612426ApInEStNaNNaNstreet space permitissued6/16/20176/16/20176/16/2017NaN6/16/2017NaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2Pacific Heights94115.0(37.78788490521045, -122.43641930200963)1.467270e+12
32.01308E+118otc alterations permit8/16/20134267292607AbRyAnTStNaNNaNcomply with nov 200009226 and 200346303 remoe e non permitted shed at the rear for unit 2607a. remodel bath, kitchen, new bedroom . unit 2607: remodel bathroom, kitchen, new bedroom, and bath. new lighting and fixtures for both unitcomplete7/24/20148/16/201311/12/20137/24/201411/12/2013NaN2.02.0NaNNaN11/7/201455000.080000.0apartments4.0apartments4.02.0NaN5.0wood frame (5)5.0wood frame (5)NaN9Mission94110.0(37.75241643936911, -122.40877419451499)1.314500e+12
42.01307E+118otc alterations permit7/26/2013151837535A26tHAv0.0NaNreplace 2 windows in bathroom not visible; no structural changes max u factor .40complete12/10/20137/26/20137/26/201312/10/20137/26/2013NaN2.02.0NaNNaN7/21/20141988.01988.02 family dwelling2.02 family dwelling2.00.0NaN5.0wood frame (5)5.0wood frame (5)NaN1Outer Richmond94121.0(37.77928233382438, -122.48608415176169)1.312290e+12
52.0161E+118otc alterations permit10/17/2016564342118AvIrGiNiAAvNaNNaNkitchen remodeling, replace existing cabinets with new units, intsll new sink, faucet, countertop, led lights, install new window, install wall ceiling insulation, drywall, painting. provide plumbing to islandissued10/18/201610/17/201610/18/2016NaN10/18/2016NaN3.03.0NaNNaN10/13/20178000.025000.02 family dwelling2.02 family dwelling2.00.0NaN5.0wood frame (5)5.0wood frame (5)NaN9Bernal Heights94110.0(37.74224320174262, -122.41981352499106)1.441230e+12
62.01406E+118otc alterations permit6/11/2014364761142AgUeRrErOStNaNNaNfor administrative purposes. to issue certificate of occupancy for final inspection. ref#200302258249. permitted as 1142 guerrero st to be rectified as 1142 a guerrero st.complete5/27/20156/11/20146/11/20145/27/20156/11/2014NaN1.01.0NaNNaN6/6/20151.01.01 family dwelling1.01 family dwelling1.00.0NaN5.0wood frame (5)5.0wood frame (5)NaN8Mission94110.0(37.752837734228955, -122.4233184555373)1.344940e+12
7M6191078otc alterations permit9/1/20154143452662A22nDSt0.0NaNstreet space permitissued9/1/20159/1/20159/1/2015NaN9/1/2015NaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN9Mission94110.0(37.75630632321998, -122.4080145673593)1.394150e+12
8M7481278otc alterations permit12/20/2016112811139AsCoTtStNaNNaNstreet space permitissued12/20/201612/20/201612/20/2016NaN12/20/2016NaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN5Western Addition94115.0(37.78025155712736, -122.43783693425848)1.447990e+12
92.01501E+118otc alterations permit1/30/20151838014A1499A15tHAvNaNNaNrevise pa# 201411171643. change of address on permit. change to 1499a 15th ave.complete2/6/20151/30/20151/30/20152/6/20151/30/2015NaN2.02.0NaNNaN1/25/20161.01.0apartments4.0apartments4.00.0NaN5.0wood frame (5)5.0wood frame (5)NaN7Inner Sunset94122.0(37.760154807972945, -122.4728804480781)1.369530e+12
Permit NumberPermit TypePermit Type DefinitionPermit Creation DateBlockLotStreet NumberStreet Number SuffixStreet NameStreet SuffixUnitUnit SuffixDescriptionCurrent StatusCurrent Status DateFiled DateIssued DateCompleted DateFirst Construction Document DateStructural NotificationNumber of Existing StoriesNumber of Proposed StoriesVoluntary Soft-Story RetrofitFire Only PermitPermit Expiration DateEstimated CostRevised CostExisting UseExisting UnitsProposed UseProposed UnitsPlansetsTIDF ComplianceExisting Construction TypeExisting Construction Type DescriptionProposed Construction TypeProposed Construction Type DescriptionSite PermitSupervisor DistrictNeighborhoods - Analysis BoundariesZipcodeLocationRecord ID
1989002.01506E+118otc alterations permit6/2/20157101A7736NaNhUrOnAvNaNNaNreroofingcomplete3/11/20166/2/20156/2/20153/11/20166/2/2015NaN2.02.0NaNNaN5/27/20166000.06000.01 family dwelling1.01 family dwelling1.00.0NaN5.0wood frame (5)5.0wood frame (5)NaN11Outer Mission94112.0(37.71192805393076, -122.4500944316179)1.383550e+12
198901M8710278otc alterations permit12/28/20171870231514NaN25tHAvNaNNaNstreet spaceissued12/28/201712/28/201712/28/2017NaN12/28/2017NaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4Sunset/Parkside94122.0(37.75921574765919, -122.48300451049278)1.492020e+12
1989022.01609E+118otc alterations permit9/20/20162130A006G78NaNcRaGmOnTAvNaNNaNreplace (e) tub with walk in tub, 20 amp circuit, gfci outletcomplete9/28/20169/20/20169/20/20169/28/20169/20/2016NaN2.02.0NaNNaN9/15/201717900.017900.01 family dwelling1.01 family dwelling1.00.0NaN5.0wood frame (5)5.0wood frame (5)NaN7Inner Sunset94116.0(37.74973733662819, -122.4671301493082)1.438110e+12
1989032.01407E+118otc alterations permit7/16/201425282466NaNcReStLaKeDrNaNNaNcut a door and a staircase for access to backyard.build deck next to staircase.complete1/23/20157/16/201410/28/20141/23/201510/28/2014Y2.02.0NaNNaN10/23/20154500.06000.01 family dwelling1.01 family dwelling1.02.0NaN5.0wood frame (5)5.0wood frame (5)NaN4Sunset/Parkside94132.0(37.73542953855242, -122.48149902038024)1.349090e+12
198904M6510478otc alterations permit12/30/201561768342NaNhArKnEsSAvNaNNaNstreet spaceissued12/30/201512/30/201512/30/2015NaN12/30/2015NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN10Visitacion Valley94134.0(37.71765127619624, -122.40342209330782)1.407980e+12
1989052.01604E+113additions alterations or repairs4/5/20167295213251NaN20tHAvNaNNaNti for space 129,130, 132 partitions, mep, shelving, stonestown mall. ab -56 compliance 2014-0627-9771complete11/22/20164/5/20167/18/201611/22/20167/18/2016NaN2.02.0NaNNaN7/3/2019325000.0928009.2retail sales0.0retail sales0.02.0NaN2.0constr type 22.0constr type 2NaN7Lakeshore94132.0(37.728556952954136, -122.47676641508518)1.418500e+12
1989062.0151E+118otc alterations permit10/27/20154009001A1919NaNmArIpOsAStNaNNaNinstall new 2" underground combination fire main. ref app#201402108156issued10/27/201510/27/201510/27/2015NaN10/27/2015NaN3.03.0NaNY10/21/20161200.09600.01 family dwelling1.01 family dwelling1.02.0NaN5.0wood frame (5)5.0wood frame (5)NaN10Potrero Hill94107.0(37.76328445631136, -122.40287014554292)1.400890e+12
1989072.01607E+118otc alterations permit7/29/20164262201341NaNsAn bRuNoAvNaNNaNreplace rotten wooden moldings in kind front stairs less than 50% replaceissued7/29/20167/29/20167/29/2016NaN7/29/2016NaN2.02.0NaNNaN7/24/20171900.01900.01 family dwelling1.01 family dwelling1.00.0NaN5.0wood frame (5)5.0wood frame (5)NaN10Mission94110.0(37.75260530951628, -122.40400191084352)1.431900e+12
1989082.01701E+118otc alterations permit1/6/20171467660NaNbRoAdWaYNaNNaNNaNrevision to pa 2016-0926-8773; remove (e) storefront & doors, remove floor, ceiling & wall finishes for new. new storefront, (n) wall & ceiling finishes, (n) flooring with waterproofing in a (n) alcove. mep under pa#201503100377issued2/1/20171/6/20172/1/2017NaN2/1/2017NaN2.02.0NaNNaN1/27/20181.01.0retail sales9.0retail sales9.02.0NaN5.0wood frame (5)5.0wood frame (5)NaN3Chinatown94133.0(37.79800446861674, -122.4080339831039)1.449660e+12
1989092.01604E+118otc alterations permit4/25/2016370856525NaNmArKeTStNaNNaN16th floor - (4) evacuation plans.issued4/25/20164/25/20164/25/2016NaN4/25/2016NaN38.038.0NaNY4/20/20171600.01600.0office0.0office0.02.0NaN1.0constr type 11.0constr type 1NaN6Financial District/South Beach94105.0(37.79040639954478, -122.39927546096968)1.420790e+12

Duplicate rows

Most frequently occurring

Permit NumberPermit TypePermit Type DefinitionPermit Creation DateBlockLotStreet NumberStreet Number SuffixStreet NameStreet SuffixUnitUnit SuffixDescriptionCurrent StatusCurrent Status DateFiled DateIssued DateCompleted DateFirst Construction Document DateStructural NotificationNumber of Existing StoriesNumber of Proposed StoriesVoluntary Soft-Story RetrofitFire Only PermitPermit Expiration DateEstimated CostRevised CostExisting UseExisting UnitsProposed UseProposed UnitsPlansetsTIDF ComplianceExisting Construction TypeExisting Construction Type DescriptionProposed Construction TypeProposed Construction Type DescriptionSite PermitSupervisor DistrictNeighborhoods - Analysis BoundariesZipcodeLocationRecord ID# duplicates
12.01302E+114sign - erect2/5/2013141250NaNbEaChStNaNNaNto attach signs to existing awningcomplete9/18/20132/5/20132/5/20139/18/20132/5/2013NaN4.0NaNNaNNaN1/31/201410000.010000.0retail salesNaNNaNNaN2.0NaN1.0constr type 1NaNNaNNaN3North Beach94133.0(37.80805300064998, -122.41324762431046)1.295230e+124
592.01609E+114sign - erect9/21/20167295213251NaN20tHAvNaNNaNvictoria's secret wall mounted electrical single faced wall signissued9/21/20169/21/20169/21/2016NaN9/21/2016NaN2.0NaNNaNNaN9/16/20172000.02000.0retail salesNaNNaNNaN2.0NaN2.0constr type 2NaNNaNNaN7Lakeshore94132.0(37.728556952954136, -122.47676641508518)1.438210e+124
782.01711E+114sign - erect11/27/20173531200NaNlArKiNStNaNNaNto erect one non-electric, non-illuminated, single faced, wall sign.filed11/27/201711/27/2017NaNNaNNaNNaN4.0NaNNaNNaNNaN3000.0NaNmuseum0.0NaNNaN2.0NaN1.0constr type 1NaNNaNNaN6Tenderloin94102.0(37.78021549250912, -122.41602577118189)1.488290e+124
02.01302E+114sign - erect2/11/20133930A22300NaN16tHStNaNNaNinstall one illuminated wall sign for "vitamin shoppe"complete6/26/20132/11/20132/28/20136/26/20132/28/2013NaN2.0NaNNaNNaN2/23/20142000.02000.0retail sales0.0NaNNaN2.0NaN1.0constr type 1NaNNaNNaN10Mission94103.0(37.76660897553399, -122.40911776717232)1.295810e+123
102.01309E+114sign - erect9/17/20131517376333NaNgEaRyBlNaNNaNerect electric signcomplete11/19/20139/17/20139/17/201311/19/20139/17/2013NaN1.0NaNNaNNaN9/12/20146500.06500.0retail sales0.0NaNNaN2.0NaN3.0constr type 3NaNNaNNaN1Outer Richmond94121.0(37.779635697353044, -122.48739021723274)1.317860e+123
262.01404E+112new construction wood frame4/25/2014624319590NaNlElAnDAvNaNNaNto erect 3 stories, no basement, single family dwelling.filed4/25/20144/25/2014NaNNaNNaNNaNNaN3.0NaNNaNNaN350000.0NaNNaNNaN1 family dwelling1.02.0NaNNaNNaN5.0wood frame (5)Y10Visitacion Valley94134.0(37.71511329299603, -122.41528882720623)1.339820e+123
552.01608E+112new construction wood frame8/4/2016537771159NaNcHaRtEr oAkAvNaNNaNerect 3 story, no basement, single family dwelling.filed8/4/20168/4/2016NaNNaNNaNNaNNaN3.0NaNNaNNaN300000.0NaNNaNNaN1 family dwelling1.02.0NaNNaNNaN5.0wood frame (5)Y10Bayview Hunters Point94124.0(37.73537008200893, -122.404282097856)1.432500e+123
94M6022888otc alterations permit7/9/20151859A1140NaNlUrLiNeStNaNNaNNaNissued7/9/20157/9/20157/9/2015NaN7/9/2015NaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN7Inner Sunset94122.0(37.75941893593247, -122.47089493049447)1.387960e+123
22.01302E+114sign - erect2/5/2013141250NaNbEaChStNaNNaNto erect 3 blade signs with intergrated lighting, signs facing jeffersoncomplete9/17/20132/5/20132/5/20139/17/20132/5/2013NaN4.0NaNNaNNaN1/31/201410000.010000.0retail sales0.0NaNNaN2.0NaN1.0constr type 1NaNNaNNaN3North Beach94133.0(37.80805300064998, -122.41324762431046)1.295290e+122
32.01302E+118otc alterations permit2/1/2013628043806NaNrUsSiAAvNaNNaNinstall shake vinyl siding on back of house only not visible from the street . no structural changescomplete5/8/20132/1/20132/1/20135/8/20132/1/2013NaN3.03.0NaNNaN1/27/20147935.07935.02 family dwelling2.02 family dwelling2.00.0NaN5.0wood frame (5)5.0wood frame (5)NaN11Excelsior94112.0(37.71823948380821, -122.43070649984861)1.294940e+122